Train ffhq ldm to generate 64x64 images

[pysaba]

Is what I am doing correct? I have downloaded the 128x128 thumbnails from the ffhq repository and want to train a model that generates 64x64 images. To do this i have changed the configs/latent-diffusion/ffhq-ldm-vq-4.yaml to following( I have changed every mention of 256 to 64, marked in BOLD: model: base_learning_rate: 2.0e-06 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.0015 linear_end: 0.0195 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: image image_size: 64 channels: 3 monitor: val/loss_simple_ema unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 64 in_channels: 3 out_channels: 3 model_channels: 224 attention_resolutions:

note: this isn\t actually the resolution but

    # the downsampling factor, i.e. this corresnponds to
    # attention on spatial resolution 8,16,32, as the
    # spatial reolution of the latents is 64 for f4
    - 8
    - 4
    - 2
    num_res_blocks: 2
    channel_mult:
    - 1
    - 2
    - 3
    - 4
    num_head_channels: 32
first_stage_config:
  target: ldm.models.autoencoder.VQModelInterface
  params:
    embed_dim: 3
    n_embed: 8192
    ckpt_path: configs/first_stage_models/vq-f4/model.yaml
    ddconfig:
      double_z: false
      z_channels: 3
      **resolution: 64**
      in_channels: 3
      out_ch: 3
      ch: 128
      ch_mult:
      - 1
      - 2
      - 4
      num_res_blocks: 2
      attn_resolutions: []
      dropout: 0.0
    lossconfig:
      target: torch.nn.Identity
cond_stage_config: __is_unconditional__

data: target: main.DataModuleFromConfig params: batch_size: 42 num_workers: 5 wrap: false train: target: taming.data.faceshq.FFHQTrain params: size: 64 validation: target: taming.data.faceshq.FFHQValidation params: size: 64

lightning: callbacks: image_logger: target: main.ImageLogger params: batch_frequency: 5000 max_images: 8 increase_log_steps: False

trainer: benchmark: True

[GrandpaXun242]

From my perspective , the image_size of target named ldm.models.diffusion.ddpm.LatentDiffusion is latent's size. it isn't real image size.